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Enhancing data representation in forging processes: Investigating discretization and R-adaptivity strategies with Proper Orthogonal Decomposition reduction

Article dans une revue avec comité de lecture
Author
ccURIBE, David
107452 Laboratoire de Conception Fabrication Commande [LCFC]
ccDURAND, Camille
107452 Laboratoire de Conception Fabrication Commande [LCFC]
ccBAUDOUIN, Cyrille
107452 Laboratoire de Conception Fabrication Commande [LCFC]
301320 École Nationale Supérieure d'Arts et Métiers [ENSAM]
ccBIGOT, Regis
107452 Laboratoire de Conception Fabrication Commande [LCFC]

URI
http://hdl.handle.net/10985/25821
DOI
10.1016/j.finel.2024.104276
Date
2024-12
Journal
Finite Elements in Analysis and Design

Abstract

Effective data reduction techniques are crucial for enhancing computational efficiency in complex industrial processes such as forging. In this study, we investigate various discretization and mesh adaptivity strategies using Proper Orthogonal Decomposition (POD) to optimize data reduction fidelity in forging simulations. We focus particularly on r-adaptivity techniques, which ensure a consistent number of elements throughout the field representation, filling a gap in existing research that predominantly concentrates on h-adaptivity. Our investigation compares isotropic mesh approaches with anisotropic mesh adaptations, including gradient-based, isolines-based, and spring-energy-based methods. Through numerical simulations and analysis, we demonstrate that these anisotropic techniques provide superior fidelity in representing deformation fields compared to isotropic meshes. These improvements are achieved while maintaining a similar level of model reduction efficiency. This enhancement in representation leads to improved data reduction quality, forming the foundation for data-driven models. This research contributes to advancing the understanding of mesh adaptivity approaches and their potential applications in data-driven modeling across various industrial domains.

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